Method and apparatus for identifying a transport mode of probe data

ABSTRACT

An approach is provided for determining probe data generated by a device travelling on a road segment is for pedestrian travel. A lane matching platform determines a speed of a probe point. The lane matching platform also determines a spatial distance of the probe point from a center line vector of a road segment. The lane matching platform also determines an allowed transport mode for the road segment. The lane matching platform further identifies the transport mode of the probe point based on the speed, the location of the probe point with respect to the center line, and the allowed transport mode. The transport mode, the allowed transport mode, or a combination thereof includes a car transport mode or a pedestrian transport mode

BACKGROUND

Service providers and device manufacturers (e.g., wireless, cellular,etc.) are continually challenged to deliver value and convenience toconsumers by, for example, providing compelling network services. Onearea of interest has been the development of services for trackingdevice users as they travel along roadways and/or navigate to differentdestinations, including global positioning system (GPS) based services.For example, a routing service may collect and process GPS probe data(e.g., probe points) generated by a device during driving of a vehicleto determine the vehicle's location and generate corresponding mappingor routing data. Unfortunately, service providers are limited in theirability to distinguish between GPS probe data corresponding to apedestrian mode of travel versus vehicular travel. Resultantly, servicesrelying on analysis of this GPS probe data for navigation processing arecannot accurately determine pedestrian mobility and travel flows for agiven road segment.

SOME EXAMPLE EMBODIMENTS

Therefore, there is a need for an approach for identifying a transportmode of a probe point.

According to one embodiment, a method comprises determining, by theprocessor, a speed of a probe point. The method also comprisesdetermining, by the processor, a spatial distance of the probe pointfrom a center line vector of a road segment. The method also comprisesdetermining, by the processor, an allowed transport mode for the roadsegment. The method further comprises identifying, by the processor, thetransport mode of the probe point based on the speed, the location ofthe probe point with respect to the center line, and the allowedtransport mode. The transport mode, the allowed transport mode, or acombination thereof includes a car transport mode or a pedestriantransport mode.

According to another embodiment, an apparatus comprises at least oneprocessor, and at least one memory including computer program code forone or more computer programs, the at least one memory and the computerprogram code configured to, with the at least one processor, cause, atleast in part, the apparatus to determine a speed of a probe point. Theapparatus is also caused to determine a spatial distance of the probepoint from a center line vector of a road segment. The apparatus is alsocaused to determine an allowed transport mode for the road segment. Theapparatus is further caused to identify the transport mode of the probepoint based on the speed, the location of the probe point with respectto the center line, and the allowed transport mode. The transport mode,the allowed transport mode, or a combination thereof includes a cartransport mode or a pedestrian transport mode.

According to another embodiment, a computer-readable storage mediumcarries one or more sequences of one or more instructions which, whenexecuted by one or more processors, cause, at least in part, anapparatus to determine a speed of a probe point. The apparatus is alsocaused, at least in part, to determine a spatial distance of the probepoint from a center line vector of a road segment. The apparatus is alsocaused, at least in part, to determine an allowed transport mode for theroad segment. The apparatus is further caused, at least in part, toidentify, by the processor, the transport mode of the probe point basedon the speed, the location of the probe point with respect to the centerline, and the allowed transport mode. The transport mode, the allowedtransport mode, or a combination thereof includes a car transport modeor a pedestrian transport mode.

According to another embodiment, an apparatus comprises means forcausing, at least in part, a determining of a speed of a probe point.The apparatus also comprises means for causing, at least in part, adetermining of a spatial distance of the probe point from a center linevector of a road segment. The apparatus also comprises means fordetermining an allowed transport mode for the road segment. Theapparatus further comprises means for, at least in part, identifying thetransport mode of the probe point based on the speed, the location ofthe probe point with respect to the center line, and the allowedtransport mode. The transport mode, the allowed transport mode, or acombination thereof includes a car transport mode or a pedestriantransport mode.

In addition, for various example embodiments of the invention, thefollowing is applicable: a method comprising facilitating a processingof and/or processing (1) data and/or (2) information and/or (3) at leastone signal, the (1) data and/or (2) information and/or (3) at least onesignal based, at least in part, on (or derived at least in part from)any one or any combination of methods (or processes) disclosed in thisapplication as relevant to any embodiment of the invention.

For various example embodiments of the invention, the following is alsoapplicable: a method comprising facilitating access to at least oneinterface configured to allow access to at least one service, the atleast one service configured to perform any one or any combination ofnetwork or service provider methods (or processes) disclosed in thisapplication.

For various example embodiments of the invention, the following is alsoapplicable: a method comprising facilitating creating and/orfacilitating modifying (1) at least one device user interface elementand/or (2) at least one device user interface functionality, the (1) atleast one device user interface element and/or (2) at least one deviceuser interface functionality based, at least in part, on data and/orinformation resulting from one or any combination of methods orprocesses disclosed in this application as relevant to any embodiment ofthe invention, and/or at least one signal resulting from one or anycombination of methods (or processes) disclosed in this application asrelevant to any embodiment of the invention.

For various example embodiments of the invention, the following is alsoapplicable: a method comprising creating and/or modifying (1) at leastone device user interface element and/or (2) at least one device userinterface functionality, the (1) at least one device user interfaceelement and/or (2) at least one device user interface functionalitybased at least in part on data and/or information resulting from one orany combination of methods (or processes) disclosed in this applicationas relevant to any embodiment of the invention, and/or at least onesignal resulting from one or any combination of methods (or processes)disclosed in this application as relevant to any embodiment of theinvention.

In various example embodiments, the methods (or processes) can beaccomplished on the service provider side or on the mobile device sideor in any shared way between service provider and mobile device withactions being performed on both sides.

For various example embodiments, the following is applicable: Anapparatus comprising means for performing the method of the claims.

Still other aspects, features, and advantages of the invention arereadily apparent from the following detailed description, simply byillustrating a number of particular embodiments and implementations,including the best mode contemplated for carrying out the invention. Theinvention is also capable of other and different embodiments, and itsseveral details can be modified in various obvious respects, all withoutdeparting from the spirit and scope of the invention. Accordingly, thedrawings and description are to be regarded as illustrative in nature,and not as restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments of the invention are illustrated by way of example, andnot by way of limitation, in the figures of the accompanying drawings:

FIG. 1 is a diagram of a system for identifying a transport mode of aprobe point, according to one embodiment;

FIG. 2A is a diagram depicting a spatial distribution of probe datagenerated for a road segment having multiple lanes, according to oneembodiment;

FIG. 2B is a diagram depicting a Hidden Markov Model, according to oneembodiment;

FIG. 2C is a diagram depicting possible transport mode transitions perHidden Markov Modeling, according to one embodiment;

FIG. 2D is a diagram depicting a speed probability per Hidden MarkovModeling, according to one embodiment;

FIG. 2E is a diagram depicting a lane-level probability per HiddenMarkov Modeling, according to one embodiment;

FIG. 3 is a diagram of a geographic database, according to oneembodiment;

FIG. 4 is a diagram of the components of a lane matching platform,according to one embodiment;

FIGS. 5 through 8 are flowcharts of a process for identifying atransport mode of a probe point, according to various embodiments;

FIGS. 9A and 9B are diagrams depicting a use case example of probe databeing matched to pedestrian and vehicular travel for presentment to adevice, according to various embodiments;

FIG. 10 is a diagram of hardware that can be used to implement anembodiment of the invention;

FIG. 11 is a diagram of a chip set that can be used to implement anembodiment of the invention; and

FIG. 12 is a diagram of a mobile terminal (e.g., handset) that can beused to implement an embodiment of the invention.

DESCRIPTION OF SOME EMBODIMENTS

Examples of a method, apparatus, and computer program for determiningprobe data generated by a device travelling on a road segment is forpedestrian travel is disclosed. In the following description, for thepurposes of explanation, numerous specific details are set forth inorder to provide a thorough understanding of the embodiments of theinvention. It is apparent, however, to one skilled in the art that theembodiments of the invention may be practiced without these specificdetails or with an equivalent arrangement. In other instances,well-known structures and devices are shown in block diagram form toavoid unnecessarily obscuring the embodiments of the invention.

FIG. 1 is a diagram of a system capable of identifying a transport modeof a probe point, according to one embodiment. For the purpose ofillustration herein, a road segment may include a roadway link thatextends about a geographic area. Each road segment includes at least abeginning node and a terminating node, where each node serves as aconnection point (e.g., an intersection, an entranceway) to another roadsegment, a terminating point of the current road segment, etc. Thenetwork of road segments and/or nodes may be stored to a geographicinformation system/database (e.g., database 109) for use by variouslocation based services. Road segments may also be associated withattributes, such as geographic coordinates, street names, addressranges, speed limits, turn restrictions at intersections, and othernavigation related attributes, as well as points of interest (POIs),such as gasoline stations, hotels, restaurants, museums, stadiums,offices, automobile dealerships, auto repair shops, buildings, stores,parks, bridges, tunnels, etc. In certain instances, attributes regardingthe lanes of a road segment, such as lane count, direction and type(e.g., pedestrian, bike, car, car pool) may also be maintained by thegeographic information system.

Location based services such as mapping and navigation services ortraffic services may access the above described database to providereal-time navigation instructions, visual maps, travel recommendationsor the like to requesting travelers. The ability of service providers togenerate and convey such information is based on the service's abilityto track the real-time location, position, direction/bearing, time,etc., of devices traveling the road segment. Typically, these servicesrely upon global positioning service (GPS) probe data for enabling suchtracking, wherein a device such as an onboard navigator or a vehicle ormobile phone serves as a probing mechanism. Under this scenario, theprobe employs various sensors to collect the relevant tracking data andcommunicates this information as probe data to one or more GPSsatellites available to the service providers. GPS probe data may bepersistently shared as one or more data packets or strings thereof forspecifying the current latitude, longitude, altitude, heading, speed,precision, etc., of the vehicle.

Unfortunately, service providers are limited in their ability todistinguish between probe data corresponding to pedestrian travel versusvehicular travel. For example, it is difficult for location basedservices to process probe data produced on pedestrian lanes due tomap-matching problems—i.e., probes on pedestrian lanes easily getmatched to the car lanes. Close proximity of a pedestrian lane andadjacent vehicle lane may lead to spatial errors between probes wheremovement of a pedestrian (e.g., person) is interpreted as driving incongestion, bike travel, etc. Thus, noisy GPS probes cannot be readilymap-matched to pedestrian lanes, which further inhibits the ability ofservices to obtain analytic insights of pedestrian travel flow for agiven road segment.

To address this issue, system 100 of FIG. 1 enables location basedservices (e.g., navigation systems, routing applications) used by avehicle or pedestrian in connection with user equipment (e.g., a mobiledevice) to accurately determine pedestrian travel from vehicle travelalong a road segment. In one embodiment, the system 100 can determine anumber of lanes there are for a road segment based on clustering ofhistoric probe points for the road segment. This may include, forexample, determining the respective spatial distances of each historicprobe point within the cluster for a given period from a line vectorplaced in the direction of travel (e.g., virtually or assumed) at themidpoint of the road segment and analyzing the probes respective spatialdistributions. The system 100 may analyze the peaks of a multi-modalitypattern as generated per respective spatial distances of probes withinthe cluster, wherein the peaks indicate the most commonly travelledspatial distances and thus the most likely lane-partitions of the roadsegment.

In another embodiment, the system 100 may further determine a transportmode and/or state to associate with a device (e.g., user equipment (UE)103) and/or probe points generated during travel along a road segment.The transport mode may correspond to an operational state, a travelmodality, a vehicle or UE type, etc. This includes a pedestriantransport mode or vehicle transport mode for indicating a form or typeof travel. The corresponding transport mode may also specify or beassociated with a type of lane travelled on by a pedestrian or vehiclerelative to a current set of probe points produced, i.e., a pedestrianlane or vehicle lane. By way of example, the “pedestrian state”corresponds to walking, running or any form/mode of travel executed by aperson without the support of mechanical, motorized or other vehicularor transport means. In contrast, the “vehicle state” corresponds to anyform/mode of travel involving mechanical and/or motorized vehicular orother transport means (e.g., bicycle, car, hovercraft). The system 100infers a specific lane and lane-level position of the device and/orprobe points during travel on the road segment based on prior analyzedspatial distribution clusters as well as hidden state modeling andinference processing. As will be discussed further later herein, thehidden state modeling and inference processing may be performed inaccordance with known Hidden Markov Modeling (HMM) and related Viterbialgorithm processing techniques respectively.

In one embodiment, a lane matching platform 107 of system 100 operatesin connection with one or more user equipment (UE) 103. By way ofexample, the UE 103 may be an in-vehicle navigation system as operatedby one or more vehicles 101 a-101 n (collectively referred to herein asvehicles 101). Though depicted as automobiles, it is contemplated thevehicles 101 can be any type of transportation vehicle manned orunmanned (e.g., planes, aerial drone vehicles, motor cycles, boats,bicycles, etc.). Alternatively, the UE 103 may be operated by one ormore pedestrians 102 a-102 n (collectively referred to herein aspedestrians 102) as a personal navigation device (“PND”), a portablenavigation device, a cellular telephone, a mobile phone, a personaldigital assistant (“PDA”), a watch, a camera, a computer and/or anyother device that supports location based services, i.e., digitalrouting and map display. It is contemplated, in future embodiments, thata device employed by a pedestrian may be interfaced with an on-boardnavigation system of an autonomous vehicle or physically connected tothe vehicle for serving as the navigation system. Also, the UE 103 maybe configured to access a communication network 105 by way of any knownor still developing communication protocols.

Also, the UE 103 may be configured with applications 111 a-111 n (alsocollectively referred to as applications 111) for interacting with oneor more content providers 119 a-119 n, services 115 a-115 n of a serviceplatform 117, or a combination thereof. The application 111 may be anytype of application that is executable at the vehicle 101 and/or the UE103, such as mapping applications, location-based service applications,navigation applications, content provisioning services, camera/imagingapplications, media player applications, social networking applications,calendar applications, and the like. In one embodiment, the application111 may act as a client for the lane matching platform 107 and performone or more functions of the lane matching platform 107 alone or incombination with the lane matching platform 107. The content providers119 a-119 n (collectively referred to as content providers 119) andservices 115 a-115 n (collectively referred to as services 115) relyupon the gathering of probe data, such as at the request of theapplication 111, for executing its functions and/or services.

The UEs 103 may be configured with various sensors (not shown forillustrative convenience) for acquiring and/or generating probe dataregarding a vehicle 101, a driver, a pedestrian 102, other vehicles,conditions regarding the driving environment or roadway, etc. Forexample, sensors may be used as GPS receivers for interacting with oneor more satellites 113 to determine and track the current speed,position and location of a vehicle travelling along a roadway. Inaddition, the sensors may gather tilt data (e.g., a degree of incline ordecline of the vehicle during travel), motion data, light data, sounddata, image data, weather data, temporal data and other data associatedwith the vehicle and/or UEs 103 thereof. Still further, the sensors maydetect local or transient network and/or wireless signals, such as thosetransmitted by nearby devices during navigation of a vehicle along aroadway (Li-Fi, near field communication (NFC)) etc. This may include,for example, network routers configured within a premise (e.g., home orbusiness), another UE 103 or vehicle or a communicable traffic system(e.g., traffic lights, traffic cameras, traffic signals, digitalsignage).

It is noted therefore that the above described data may be transmittedto the satellites 113 via communication network 105 as GPS probe dataaccording to any known wireless communication protocols. For example,each vehicle 101 and/or UE 103 may be assigned a unique probe identifier(probe ID) for use in reporting or transmitting said probe datacollected by the vehicles 101 and UEs 103. In one embodiment, eachvehicle 101 and/or UE 103 is configured to report probe data as probepoints, which are individual data records collected at a point in timethat records telemetry data. Probe points can be collected by the system100 from the vehicles 101 and/or UEs 103 in real-time, in batches,continuously, or at any other frequency requested by the system 100over, for instance, the communication network 105 for processing by thelane matching platform 107.

In one embodiment, the lane matching platform 107 retrieves aggregatedprobe points gathered and/or generated by UE 103 resulting from thetravel of pedestrians 102 and vehicles 101 on a road segment. Thetrajectories database 109 stores a plurality of probe points andsequences thereof generated by different UE 103 over a period relativeto a common road segment. A sequence of historic probe points specifiesa trajectory—i.e., a path traversed by a probe on a road segment orother connected road segments over a period. The lane matching platform107 analyzes the probe points to determine a spatial distance metric foreach probe point. The spatial distribution may be further determined byaccessing road segment information, such as from a geographicalinformation system (i.e., a service 115), for specifying the featuresand attributes of the road segment. By way of example, the lane matchingplatform 107 performs non-Gaussian statistical analysis of aggregatedhistorical probe point spatial distributions for a given road segment toidentify or approximate its lanes. This exemplary process is depicted inFIG. 2.

FIG. 2A is a diagram depicting a spatial distribution of probe datagenerated for a road segment having multiple lanes, according to oneembodiment. The lane matching platform 107 determines the number oflanes of the road segment based on statistical analysis. Per thisscenario, a two-dimensional visual representation of a road segment 200is shown. The visual representation of the road segment 200 may beobtained by the lane matching platform 107 from a mapping service andthe center line vector 207 may be placed at a midpoint of the roadsegment 200. In addition, the lane matching platform 107 may access thetrajectories database 109 to acquire historical probe points generatedby a plurality of UE 103 during travel on the road segment 200 for aperiod. The probe points (i.e., probe points 209) are shown as dotsdispersed about the road segment 200. Each probe point corresponds to aspecific location, position, speed, bearing, etc., of UE 103 at aspecific time.

In one embodiment, the lane matching platform 107 places a center linevector 207 (labeled as the y-axis) at the midpoint of the road segment200. Placement at the midpoint may be based on known attributes of theroad segment, approximation, etc. The center line vector 207 matches thedirection of travel of the respective historical probe points and theroad segment 200. For the purpose of illustration, an x-axis 219 is alsoshown for depicting a negative/positive plane of the road segment.

In one embodiment, the lane matching platform 107 further determines aspatial distance d from the center line vector 207 of the road segment200 to each of the plurality of probe points, i.e., probe points 209. Assuch, a cross-section of probes corresponding to a given spatialdistance d from the center line vector 207 may be captured. By way ofexample, a spatial distance 205 represents a positive distance from thecenter line 207 to a probe point 221 while a spatial distance 223represents a negative distance from the center line 207 to a probe point225. The matching platform 107 determines the spatial distance of eachof the probe points. In another embodiment, the matching platform 107clusters the historical probe points into spatial distribution clustersbased on their respective spatial distances. By way of example, theclusters are made up of historical probe points having similar spatialdistances or of a value within a predetermined range of one another.

Clustering of the historical probe points results in lane-level groupingof probe points given that vehicles and pedestrians do not typicallytravel directly on lane lines. Also, pedestrians are typically found onthe extremes of a road segment and not the center. Thus, the lanematching platform 107 may analyze the probe point distribution along agiven road segment 200 and infer specific lane attributes and/or thestatus of a vehicle or pedestrian associated with the historical probepoints.

In one embodiment, lane partitions may be determined based onobservation of a spatial distance from the center line, as depicted pera Gaussian distribution model 210. By way of example, the Gaussiandistribution model 210 is rendered for example a ahistogram/multi-modality view 209 for indicating peaks and valleys ofprobe points. Hence, a given cross-section of the exemplary distributionmodel 210 corresponding to a spatial distance d represents a number ofprobe points, the lower peaks representing the fewest number while thehighest peak represents the highest number of probe points. In thiscase, cross-sections 212 and 214 represent peak probe-pointsdistributions labeled 1 and 2 respectively per model 210, while thecross-sections between 212 and 214 represent lower distributions. Thus,this multi-modality pattern gives an indication of lane-partitions onthe road as the number of clusters obtained from represents an intrinsicnumber of lanes on the road represent.

In the case of the distribution of probe points along road segment 200,the lane matching platform 107 determines the existence of four lanes211-217. As noted, each lane partition is determined based on thevariance between the highest density (clustering) of cross-sections ofhistorical probe points (common spatial distances versus the lowestdensity of historical probe points. While probe position accuracy maynot sufficiently define a given lane of a road segment 200, clusteringbased on a spatial distance from the center line vector 207 issufficient for identifying peak probe pointlocations/distributions/speed. In certain embodiments, the spatialdistribution clusters may be stored (e.g., along with the lane count) asa lane model information in association with the road segment 200. Lanemodel information may be associated with mapping information for theroad segment 200 to support subsequent inference and/or statedetermination analysis of probe points. This includes, for example,Hidden Markov Modeling (HMM), Viterbi algorithm processing or the like.

FIG. 2B is a diagram depicting a Hidden Markov Model, according to oneembodiment The lane matching platform 107 employs HMM to determine oneor more of: (1) a likelihood each probe point or sequence thereofcorrelates to the determined model as specified per the lane modelinformation; and/or (2) the most likely state and/or sequence of statesof probe points that correlate to the model. The lane matching platform107 encodes trips as HMMs, where the hidden states represent anoperational state and/or a travel modality of a probe point, representedby a function x(t) per the model 230. The function y(t) represents thecorresponding probe point for a given operational state/travel modalityx(t), thus showing a 1:1 interdependent relationship. Per the model 230,the travel modality corresponds to a “pedestrian” transport mode or a“vehicle” transport mode.

It is noted, in certain embodiments, that the transport mode may alsospecify or be associated with a type of lane travelled on by apedestrian or vehicle relative to a set of probe points produced, i.e.,a “pedestrian” lane or “vehicle” lane. Still further, the HMM model 230can be extended to handle more transport modes, vehicle types, lanetypes, etc. For example, the pedestrian state may be further uncoveredper HMM analysis as a walking or running mode while the vehicle statemay correspond to a bicycle, car, truck, bus, or taxi mode. For thepurpose of illustration herein, the exemplary embodiments contemplatethe generalized scenario wherein a probe point pertains to a pedestrianor a vehicle (or a motorized vehicle in general).

FIG. 2C is a diagram depicting possible transport mode transitions perHidden Markov Modeling, according to one embodiment. The lane matchingplatform 107 enables sequences of state (transport type) changesresulting from mixed trips to be observed. Mixed trips include thosewhere the UE 103 changes vehicle type or travel modality multiple times.An exemplary mixed trip is one where a person operates a navigation appon their smartphone while walking to their car and continues using theapplication while driving. Under this scenario, the state is determinedto transition from a pedestrian state to a vehicle state.

In one embodiment, the lane matching platform 107 determines fourtransition probabilities. The state transition probabilities are shownin Table 1 below:

TABLE 1 State-to-State Transition Definition 1) Car → Car Probability ofkeeping the same 2) Pedestrian → Pedestrian vehicle/travel modality typebetween consecutive probe points 3) Car →Pedestrian Probability ofchanging vehicle/ 4) Pedestrian→Car travel modality type betweenconsecutive probe points

The first transition possibility of Table 1 (Car→Car) is depicted inFIG. 2C as a transition from a vehicle, i.e., car 241 to the same car oranother 243, resulting from a respective sequence of probe points 249 to251 (P1→P2). The second transition possibility of Table 1(Pedestrian→Pedestrian) is as a transition from a pedestrian 245 to thesame pedestrian or another 247, resulting from a respective sequence ofprobe points 249 to 251 (P1→P2). The third transition possibility(Car→Pedestrian) is as a transition from a car 241 to a pedestrian 247,resulting from a respective sequence of probe points 249 to 251 (P1→P2).The final transition possibility of Table 1 (Pedestrian→Car) is as atransition from a pedestrian 245 to a car 243, resulting from arespective sequence of probe points 249 to 251 (P1→P2). The lanematching platform 107 analyzes up to n sequences of probe points, e.g.,probe point 253, for identifying/modeling the transport mode/statetransition probabilities.

The HMM is defined by two parameters, including a transition probabilityand observation probability. The transition probability defines thelikelihood the model (e.g., a lane model information produced by probepoints y(t)) moves from a state x(t) to a state x(t+1). This correspondsto the mixed trip scenario, as modelled in FIG. 2B.

In the case where the same vehicle/travel modality type is maintained,the determined transitions probability should be very high (e.g.0.9999). In the case where the vehicle/travel modality changes, which isallowed but unlikely, the determined probability should be very low(e.g. 0.0001). The transition probabilities are encoded in a transitionmatrix. For example, in the case of the aforementioned transitionprobabilities, the transition matrix would be:

$\quad\begin{bmatrix}0.9999 & 0.0001 \\0.0001 & 0.9999\end{bmatrix}$

In one embodiment, the lane matching platform 107 determines theobservation probability based on three factors: speed, lane-levelposition on the allowed transport mode/travel permissions for the roadsegment. The observation probability defines the likelihood of observinga datum (e.g., a probe point y(t)) given a certain state x(t) beingobserved per the lane model. This represents the likelihood of observinga probe point if the UE 103 x(t) producing it is in a vehicle state orpedestrian state. Once each probability is determined, the factors arecombined by multiplying their probabilities as shown below:

P(probe|State)=P(speed|State)*P(laneDist|State)*P(permission|State)

FIG. 2D is a diagram depicting a speed probability per Hidden MarkovModeling, according to one embodiment. The diagram is a graph showing arelationship between a speed of travel via an x-axis 263 and aprobability value via a y-axis 261. Line 265 represents the pedestrianfunction while line 267 represents the vehicle (e.g., car) function.

By way of example, the speed observation probability, given above asP(speed|State), can be determined by the lane matching platform 107based on the following assumptions:

-   -   A pedestrian has a speed within a specific range (e.g., 0 kph        and 10 kph), where speeds over an upper limit (e.g., 10 kph) are        unlikely to be executed by a pedestrian;    -   A vehicle (e.g., car) can drive at almost any speed; and    -   The probability of a pedestrian moving between 0 and 10 kph must        be higher than the probability of a vehicle driving between 0        and 10 kph        Based on these assumptions, the function for defining the        pedestrian speed observation is:

P(speed|Pedestrian)=(tan h(−½(x−10))+1.1)*0.4

This is graphically represented via line 265. It is noted that theprocess described above is provided by way of illustration and it iscontemplated that any function or process (e.g., a step function) can beused to determine P(speed|State). In one embodiment, the parameters ofthe equation above (e.g., ½, 10, 1.1, and 0.4) are adjustable. Forexample, the parameters can be adjusted so that the resultingprobability matches observed data. In yet another embodiment, theparameters can be “learned” using machine learning techniques orequivalent.

The function for defining the vehicle speed observation is a linearfunction, per line 267, that intersects the pedestrian probabilityfunction (line 265) at the upper limit (e.g., 10 Kph) at intersectionpoint 269. The vehicle speed function, line 267, then continues at aconstant value after the intersection point 269, corresponding to theincreasing speed capabilities of a vehicle. It is noted that abovedescribed speed ranges and functions may be adapted to account fordifferent vehicle types.

The travel permission (allowed transport mode) observation probability,given above as P(permission|State), is defined by the lane matchingplatform 107 as follows:

-   -   Assign a high probability value if the vehicle type is allowed        on the road segment the probe is on (e.g., 0.999); and    -   Assign a very low value otherwise (e.g., 0.001).        Strict 0 and 1 probability values are avoided by the lane        matching platform 107 to account for errors (e.g., map-matching        errors) and provide for more probabilistic variability.

The lane matching platform 107 may rely on rules information regardingthe road segment for assigning probabilities. Rules information may beacquired from a mapping service 115 in association the road segment. Forexample, mapping information for a road segment may specify a time inwhich the road segment is to be used as a bus lane versus a car lane,that the road segment is dedicated to pedestrian travel only, adedicated bike lane, a carpool lane, etc.

The lane-level position probability, given above as P(laneDist|State),is determined by the lane matching platform 107 based, in part, on thelane matching/spatial distribution approach described previously. Perthis approach, the number of lanes of a road segment are determinedbased on cross-section/clustering per the determined spatial distances.For HMM usage, the mean center of the extreme-right cluster is employedas the observation point for the pedestrian random variable (ortransport mode) and the mean center of the combined number of clusters(and hence lanes) forms the observation point for the vehicle randomvariable (or transport mode). Other additional features like speed isalso used.

FIG. 2E is a diagram depicting a lane-level probability per HiddenMarkov Modeling, according to one embodiment. In this case, a graph 270is presented for indicating a relationship between a given laneprobability on a y-axis 271 versus a lane distance on the x-axis 273.Lane distance corresponds to the determined spatial distances andlane-partitions as determined per FIG. 2A, i.e., for determining thenumber of lanes 275. The probability is based on a recognition by thelane matching platform 107 that pedestrians do not typically travel atthe center of a road. Rather, pedestrian transport typically occurs onthe extremes of a given road segment. Thus, graph 270 shows a higherprobability of pedestrian transport being inferred at greaterdistances—i.e., at extreme distance points 277 a and 277 b. In contrast,the probability of pedestrian transport is less at a shorter distancefrom the center line 271 (y-axis) of the road segment—i.e., distancepoint 277 c. A probability of pedestrian transport increases as thespatial distance from the center line vector increases, while theprobability remains the same in the case of vehicle transport as perline 279 (e.g., any lane can potentially be a vehicle lane though lesslikely at distance extremes).

In certain embodiments, inference analysis is performed by way ofViterbi algorithm processing based on the above described HMI modelingapproaches. For example, a sequence of current state transitions may beanalyzed and/or further compared against the lane model information. Asnoted previously, the lane model information specifies the spatialcluster distribution/probe point pattern for a given road segmentobserved for a given period. Resultantly, a transport mode match andcorresponding lane-level match is determined accordingly.

In certain embodiments, the lane matching platform may also determine apedestrian flow pattern based on the aggregated lane model information.The pedestrian flow pattern may specify a volume of pedestrian travel,an average speed of pedestrian travel, upstream origin(s) and downstreamdestination(s) of pedestrian travel on the road segment, etc. It isnoted that the pedestrian flow pattern can also be used as anobservation probability to improve real-time map-matching of pedestrianprobes.

The above described approach of the lane matching platform 107 may beemployed in cases where lane information regarding a road segment is notavailable or defined. Still further, the lane matching platform 107enables map matching of a current UE 103 to a specific lane positionalong with transport mode identification (pedestrian or vehicle). Basedon this, the lane matching platform 107 may operate with variousservices 115 of the services platform 117, the content providers 119, orthe like to support capabilities such as:

-   -   Traffic Estimation—the pedestrian probes points may be discarded        when computing the average speed on a road segment for achieving        a more accurate result;    -   City Planning and Pedestrian Analytics—determine average speed,        popular origin destinations, pedestrian traffic hotspots and        high density areas and any other pedestrian data analysis vital        for city planning;    -   Map Discovery—use pedestrian state identification to discover        which road segments have sidewalks, map crosswalks,        pedestrian-only small alleys, pathways inside parks, bridges and        crosswalks, etc.        Other applications and use cases may also be contemplated per        the aforementioned functions of the lane matching platform 107.

The communication network 105 of system 100 includes one or morenetworks such as a data network, a wireless network, a telephonynetwork, or any combination thereof. It is contemplated that the datanetwork may be any local area network (LAN), metropolitan area network(MAN), wide area network (WAN), a public data network (e.g., theInternet), short range wireless network, or any other suitablepacket-switched network, such as a commercially owned, proprietarypacket-switched network, e.g., a proprietary cable or fiber-opticnetwork, and the like, or any combination thereof. In addition, thewireless network may be, for example, a cellular network and may employvarious technologies including enhanced data rates for global evolution(EDGE), general packet radio service (GPRS), global system for mobilecommunications (GSM), Internet protocol multimedia subsystem (IMS),universal mobile telecommunications system (UMTS), etc., as well as anyother suitable wireless medium, e.g., worldwide interoperability formicrowave access (WiMAX), Long Term Evolution (LTE) networks, codedivision multiple access (CDMA), wideband code division multiple access(WCDMA), wireless fidelity (Wi-Fi), wireless LAN (WLAN), Bluetooth®,Internet Protocol (IP) data casting, satellite, mobile ad-hoc network(MANET), and the like, or any combination thereof.

In one embodiment, the lane matching platform 107 may be a platform withmultiple interconnected components. The lane matching platform 107 mayinclude multiple servers, intelligent networking devices, computingdevices, components and corresponding software for determining one ormore modal routes between one or more origin areas and one or moredestination areas based on trajectory data. In addition, it is notedthat the lane matching platform 107 may be a separate entity of thesystem 100, a part of the one or more services 115 of the servicesplatform 117, or included within the UE 103 (e.g., as part of theapplications 111).

The services platform 117 may include any type of service 115. By way ofexample, the services 115 may include mapping services, navigationservices, travel planning services, notification services, socialnetworking services, content (e.g., audio, video, images, etc.)provisioning services, application services, storage services,contextual information determination services, location-based services,information-based services (e.g., weather, news, etc.), etc. In oneembodiment, the services platform 117 may interact with the lanematching platform 107, the UE 103, and/or the content provider 119 toprovide the services 115.

In one embodiment, the content providers 119 may provide content or datato the vehicles 101 and/or UEs 103, the lane matching platform 107,and/or the services 115. The content provided may be any type ofcontent, such as textual content, audio content, video content, imagecontent, etc. In one embodiment, the content providers 119 may providethe one or more trajectories that contain one or more probe pointslocated within the one or more origin areas and the one or moredestination areas. In one embodiment, the content providers 119 may alsostore content associated with the vehicles 101, the UE 103, the lanematching platform 107, and/or the services 115. In another embodiment,the content providers 119 may manage access to a central repository ofdata, and offer a consistent, standard interface to data, such as atrajectories database, a repository of probe data, average travel timesfor one or more road links or travel routes (e.g., during free flowperiods, day time periods, rush hour periods, nighttime periods, or acombination thereof), speed information for at least one vehicle, othertraffic information, etc. Any known or still developing methods,techniques or processes for retrieving and/or accessing trajectory orprobe data from one or more sources may be employed by the lane matchingplatform 107.

By way of example, the vehicles 101, the UEs 103, the lane matchingplatform 107, the services platform 117, and the content providers 119communicate with each other and other components of the system 100 usingwell known, new or still developing protocols. In this context, aprotocol includes a set of rules defining how the network nodes withinthe communication network 105 interact with each other based oninformation sent over the communication links. The protocols areeffective at different layers of operation within each node, fromgenerating and receiving physical signals of various types, to selectinga link for transferring those signals, to the format of informationindicated by those signals, to identifying which software applicationexecuting on a computer system sends or receives the information. Theconceptually different layers of protocols for exchanging informationover a network are described in the Open Systems Interconnection (OSI)Reference Model.

Communications between the network nodes are typically effected byexchanging discrete packets of data. Each packet typically comprises (1)header information associated with a particular protocol, and (2)payload information that follows the header information and containsinformation that may be processed independently of that particularprotocol. In some protocols, the packet includes (3) trailer informationfollowing the payload and indicating the end of the payload information.The header includes information such as the source of the packet, itsdestination, the length of the payload, and other properties used by theprotocol. Often, the data in the payload for the particular protocolincludes a header and payload for a different protocol associated with adifferent, higher layer of the OSI Reference Model. The header for aparticular protocol typically indicates a type for the next protocolcontained in its payload. The higher layer protocol is said to beencapsulated in the lower layer protocol. The headers included in apacket traversing multiple heterogeneous networks, such as the Internet,typically include a physical (layer 1) header, a data-link (layer 2)header, an internetwork (layer 3) header and a transport (layer 4)header, and various application (layer 5, layer 6 and layer 7) headersas defined by the OSI Reference Model.

FIG. 3 is a diagram of the geographic database 109 of system 100,according to exemplary embodiments. In the exemplary embodiments, modalroutes, trajectories (sequences of probe points), road segments, lanemodel information and/or other related information can be stored,associated with, and/or linked to the geographic database 109 or datathereof. In one embodiment, the geographic database 109 includesgeographic data 301 used for (or configured to be compiled to be usedfor) mapping and/or navigation-related services, such as forpersonalized route determination, according to exemplary embodiments.For example, the geographic database 109 includes node data records 303,road segment or link data records 305, POI data records 307, modal routerecords 309, and trajectory data records 311, for example. More, feweror different data records can be provided. In one embodiment, the otherdata records (not shown) can include cartographic (“carto”) datarecords, routing data, and maneuver data. One or more portions,components, areas, layers, features, text, and/or symbols of the POI orevent data can be stored in, linked to, and/or associated with one ormore of these data records. For example, one or more portions of thetrajectories or modal routes can be matched with respective map orgeographic records via position or GPS data associations (such as usingknown or future map matching or geo-coding techniques), for example.

In exemplary embodiments, the road segment data records 305 are links orsegments representing roads, streets, or paths, as can be used in thecalculated route or recorded route information for determination of oneor more personalized routes, according to exemplary embodiments. Thenode data records 303 are end points corresponding to the respectivelinks or segments of the road segment data records 305. The road linkdata records 305 and the node data records 303 represent a road network,such as used by vehicles, cars, and/or other entities. Alternatively,the geographic database 109 can contain path segment and node datarecords or other data that represent pedestrian paths or areas inaddition to or instead of the vehicle road record data, for example.

The road/link segments and nodes can be associated with attributes, suchas geographic coordinates, street names, address ranges, speed limits,turn restrictions at intersections, and other navigation relatedattributes, as well as POIs, such as gasoline stations, hotels,restaurants, museums, stadiums, offices, automobile dealerships, autorepair shops, buildings, stores, parks, etc. The geographic database 109can include data about the POIs and their respective locations in thePOI data records 307. The geographic database 109 can also include dataabout places, such as cities, towns, or other communities, and othergeographic features, such as bodies of water, mountain ranges, etc. Suchplace or feature data can be part of the POI data records 307 or can beassociated with POIs or POI data records 307 (such as a data point usedfor displaying or representing a position of a city).

In addition, the geographic database 109 can include data aboutdetermined modal routes and their respective origin and destinationlocations in the modal route records 309. By way of example, modalroutes for different time periods and contexts (e.g., season, day of theweek, time of day, mode of transportation, etc.) can be determinedaccording the various embodiments described herein and stored in themodal route records 309 for subsequent retrieval or access. In addition,trajectory and/or probe data processed by the system 100 can be storedin the trajectory data records 311. For example, trimmed or simplifiedtrajectories can be stored in the trajectory data records 311 for laterretrieval or access.

The geographic database 109 can be maintained by the content provider119 in association with the services platform 117 (e.g., a mapdeveloper). The map developer can collect geographic data to generateand enhance the geographic database 109. There can be different waysused by the map developer to collect data. These ways can includeobtaining data from other sources, such as municipalities or respectivegeographic authorities. In addition, the map developer can employ fieldpersonnel to travel by vehicle along roads throughout the geographicregion to observe features and/or record information about them, forexample. Also, remote sensing, such as aerial or satellite photography,can be used.

The geographic database 109 can be a master geographic database storedin a format that facilitates updating, maintenance, and development. Forexample, the master geographic database 109 or data in the mastergeographic database 109 can be in an Oracle spatial format or otherspatial format, such as for development or production purposes. TheOracle spatial format or development/production database can be compiledinto a delivery format, such as a geographic data files (GDF) format.The data in the production and/or delivery formats can be compiled orfurther compiled to form geographic database products or databases,which can be used in end user navigation devices or systems.

For example, geographic data is compiled (such as into a platformspecification format (PSF) format) to organize and/or configure the datafor performing navigation-related functions and/or services, such asroute calculation, route guidance, map display, speed calculation,distance and travel time functions, and other functions, by a navigationdevice, such as by a UE 103, for example. The navigation-relatedfunctions can correspond to vehicle navigation, pedestrian navigation,or other types of navigation. The compilation to produce the end userdatabases can be performed by a party or entity separate from the mapdeveloper. For example, a customer of the map developer, such as anavigation device developer or other end user device developer, canperform compilation on a received geographic database in a deliveryformat to produce one or more compiled navigation databases.

As mentioned above, the geographic database 109 can be a mastergeographic database, but in alternate embodiments, the geographicdatabase 109 can represent a compiled navigation database that can beused in or with end user devices (e.g., vehicle 101, UE 103, etc.) toprovide navigation-related functions. For example, the geographicdatabase 109 can be used with the end user device to provide an end userwith navigation features. In such a case, the geographic database 109can be downloaded or stored on the end user device (e.g., vehicle 101,UE 103, etc.), such as in application 111, or the end user device canaccess the geographic database 109 through a wireless or wiredconnection (such as via a server and/or the communication network 105),for example.

In one embodiment, the end user device can be an in-vehicle navigationsystem, a personal navigation device (PND), a portable navigationdevice, a cellular telephone, a mobile phone, a personal digitalassistant (PDA), a watch, a camera, a computer, and/or other device thatcan perform navigation-related functions, such as digital routing andmap display. In one embodiment, the navigation device (e.g., UE 103) canbe a cellular telephone. An end user can use the device navigationfunctions such as guidance and map display, for example, and fordetermination of route information to at least one identified point ofinterest, according to exemplary embodiments.

FIG. 4 is a diagram of the components of a lane matching platform,according to one embodiment. By way of example, the lane matchingplatform 107 includes one or more components for determining tunnelspeed for a vehicle travelling through a tunnel. It is contemplated thatthe functions of these components may be combined or performed by othercomponents of equivalent functionality. In this embodiment, the travelplatform 107 includes a collection module 401, an analysis module 403, aclustering module 405, a counting module 407, and a presentation module409.

In one embodiment, the collection module 401 may query a trajectoriesdatabase 109 to retrieve one or more probe points associated with agiven road segment. In addition, the collection module 401 collectsprobe points associated with one or more devices travelling the roadsegment. As such, the collection module 301 may retrieve singular probepoints or a sequence of consecutive probe points for indicating atrajectory or probe point path. In one embodiment, the one or more probepoints indicate a position, a heading, a speed, a time, or a combinationthereof of each of the plurality of devices (e.g., the vehicle 101and/or the UEs 103).

In one embodiment, the analysis module 403 determines a speed of arespective probe point as well as allowed transport mode associated witha road segment (e.g., pedestrian only lane). In addition, the analysismodule 403 operates in conjunction with the clustering module 405 togenerate spatial distribution clusters based on the respective spatialdistances of probe points from a center line vector of the road segment.In one embodiment, the clustering module 405 stores the spatialdistribution clusters as lane model information along with mappinginformation for the road segment, such as for subsequent retrieval andanalysis.

In one embodiment, the analysis module 403 also interacts with a lanecounting module 407 to determine a number of lanes to associate with aroad segment. The lane counting module 407 identifies the number oflanes as the number of spatial distribution clusters generated by theclustering module 405. This includes determining one or morecross-sections of the partial distribution clusters. In one embodiment,the analysis module 403 also executes Hidden Markov Model analysis,Viterbi algorithm processing, etc. By way of example, the analysismodule 403 facilitates matching of UE 103 determined to be travelling ona road segment by the collection module 401 with the specific lane oftravel. Moreover, the analysis module 403 facilitates inferenceprocessing for identifying a specific transport mode to associate withthe UE 103 during travel on the road segment.

In one embodiment, the presentation module 409 presents the determinedlane-level information in association with relevant mapping information,navigation information or the like to a user interface of the requestingapplication 111. This includes, for example, data for indicating thepedestrian or vehicle transport mode associated with a UE 103 per thematching and inference process of the analysis module 403, a specificlane and lane position of the UE 103, etc. The presentation module 403may operate in connection with the collection module 401 and clusteringmodule 405 to facilitate the exchange of probe points and lane modelinformation via the communication network 105 with respect to theservices 109, content providers 115 and applications 111.

The above presented modules of the lane matching platform 107 can beimplemented in hardware, firmware, software, or a combination thereof.Though depicted as a separate entity in FIG. 1, it is contemplated thelane matching platform 107 may be implemented for direct operation byrespective UEs 103. As such, the lane matching platform 107 may generatedirect signal inputs by way of the operating system of the UE 103 forinteracting with the application 111. In another embodiment, one or moreof the modules 401-409 may be implemented for operation by respectiveUEs as a platform, cloud based service, or combination thereof.

FIGS. 5 through 8 are flowcharts of a process for determining probe datagenerated by a device travelling on a road segment is for pedestriantravel, according to various embodiments. In one embodiment, the lanematching platform 107 performs processes 500, 600, 700, 800 and 900 andis implemented in, for instance, a chip set including a processor and amemory as shown in FIG. 12.

In step 501 of process 500, the lane matching platform 107 determines aspeed of a probe point. In another step 503, the lane matching platform107 determines a spatial distance of the probe point from a center linevector of a road segment. In another step 505, the lane matchingplatform determines an allowed transport mode for the road segment. Instep 507, the lane matching platform 107 identifies the transport modeof the probe point based on the speed, the location of the probe pointwith respect to the center line, and the allowed transport mode. Asnoted previously, the allowed transport mode corresponds to a knownand/or predetermined lane and/or transport mode type for the roadsegment of a lane thereof. Furthermore, the transport mode, the allowedtransport mode, or a combination thereof includes a car transport modeor a pedestrian transport mode.

In step 601 of process 600 (FIG. 6), the lane matching platform 107clusters a plurality of historical probe points into one or more spatialdistribution clusters along a length of the road segment. In anotherstep 603, the lane matching platform 107 determines the one or morespatial distribution clusters corresponds to one or more travel lanes ofthe road segment and the center line vector is based on a center line ofthe at least one of the spatial distribution clusters. As notedpreviously, the clustering is based on commonality of the spatialdistances of respective probe points. In certain embodiments, theclustering may be performed in connection with HMM processing techniquesand stored as lane model information. Also, the lane model informationmay be provided an input or reference data for performing HMM or Viterbiprocessing. The one or more spatial distribution clusters corresponds toone or more travel lanes of the road segment and the center line vectoris based on a center line of the at least one of the spatialdistribution clusters.

In step 701 of process 700 (FIG. 7), the lane matching platform 107calculates a probability the probe point is either the car transportmode or the pedestrian transport mode based on the spatial distance. Inanother step 703, the lane matching platform 107 increases theprobability as the spatial distance from the center line vectorincreases. This corresponds to increased probability of the pedestriantransport mode as the distance from the center of the road towards theedges increases.

In step 705, the lane matching platform 107 increases a firstprobability the mode of transport is the car mode of transport based ona determination the allowed transport mode is a car transport mode. Perstep 707, the lane matching platform 107 increases a second probabilitythe mode of transport is the pedestrian mode of transport based on adetermination the allowed transport mode is a pedestrian transport mode.The transport mode of the probe point is further based on the firstprobability, the second probability, or a combination thereof.

In steps 801 of process 800 (FIG. 8), the lane matching platform 107determines a transition point between a first transport mode and asecond transport mode during the sequence. The probe point is part of asequence of probe points generated by a probe device and the sequence ofprobe points is a mixed mode sequence comprising a plurality oftransport modes. Respective transport modes of a plurality of otherprobe points occurring in the sequence of probe points before or afterthe transition point is identified based on the transport modeidentified for the probe point at the transition point. As notedpreviously, hidden state modeling may be performed to determine thetransition point of an associated probe. Still further, an inferencealgorithm may be executed against the sequence based on the lane modelinformation, such as via a Viterbi algorithm.

It is further noted, in certain embodiments, that the lane matchingplatform 107 may present the transport mode of a probe point, thedetermined transition point, etc., in conjunction with relevant mappinginformation for the corresponding road segment. Still further, the probepoints, lane model information and clusters may be aggregated andanalyzed subsequently for analysis to determine pedestrian traffic flow,road segment patterns, etc.

FIGS. 9A and 9B are diagrams depicting a use case example of probe databeing matched to pedestrian and vehicular travel for presentment to adevice, according to various embodiments. For the purpose ofillustration, the diagrams are described with respect to a user of amobile device 900 equipped with a navigation application. The navigationapplication is configured for interaction with the lane matchingplatform 107 for enabling the presentment of lane-level detail andtransport mode information to the device display 901 as the usernavigates to a desired destination 931.

In FIG. 9A, the user employs the device 900 to run a navigationapplication as they drive to a busy/popular tourist attraction 931(e.g., Buckingham Fountain). The device is equipped with sensors foracquiring, generating and/or sharing telemetry data, GPS probe points,etc., with the lane matching platform 107. Under this scenario, thenavigation application presents various road segments within azoomed-out map view for providing real-time guidance to the user of thedevice 900 to the destination 931. In this case, a combination of roadsegments 911, 913, 915 and 917 are highlighted within the map view forspecifying the recommended travel path to be driven.

Per the lane matching platform 107, prior generated lane modelinformation is already associated with the road segment. As such, thelane matching platform 107 processes the lane model information (e.g.,via known HMI processing techniques) to match the device 900 to aspecific lane of travel of the user and infer the transport mode of thedevice. Resultantly, a vehicle icon 903 is rendered to the display 901for denoting the current transport mode (state) and location/position ofthe user's vehicle relative to the map. In addition, a zoomed-in view919 is also presented, as an inset or picture-in-picture view, forproviding additional lane-level detail to the user. The zoomed-in viewshows the vehicle icon 903 as positioned in a third lane 921 of thefour-lane road segment (e.g., Wabash Ave) upon which the vehicle isdriving. A driving instruction 923 is also presented within thezoomed-in view 919 for recommending the user (driver) move to the secondlane given an impending right turn onto Harrison Street.

In this example, placement of the vehicle icon 903 into the third lane921 indicates the lane as a vehicle lane of the road segment. The firstlane 925, which corresponds to a more narrowly scaled pedestrian lane,is also presented. While not shown herein, the first lane 925 may behighlighted by the navigation application based on awareness of therespective lane types/states, i.e., to indicate driving on thepedestrian lane is prohibited.

Per this scenario, the user proceeds along the route and eventuallytravels along road segments 911, 913. The user nearly completes travelalong road segment 915 when they notice slowing traffic and anobstruction at the link to road segment 917 (e.g., Congress Parkway);the road segment onto which they are to make a right turn. They observe(or are presented with information via the navigation service or atraffic service) that the street 917 is blocked from through vehicletraffic due to a festival. As only pedestrian travel is allowed alongthe road segment 917, the user parks the vehicle at location 905 andproceeds to the destination on foot. This corresponds to a mixed tripscenario as described previously, where the lane matching platform 107remains active throughout travel for detecting a change in transportmode of the device and/or modality of travel. The interaction betweenthe device 901 and the lane matching platform 107 as a result of thestate transition is depicted in FIG. 9B.

As the user navigates along road segment 917 on foot, the lane matchingplatform 97 detects the transition from a vehicle transport mode to apedestrian transport mode. Based on this determination, the navigationapplication renders a pedestrian icon 927 to the display instead of thevehicle icon 903. The transition is also denoted via the zoomed-in view919, which presents an updated instruction 923 pertaining to the walkingtransport mode of the user. Moreover, the zoomed-in view 919 shows thepedestrian icon 927 as being placed in a first lane 929 of the roadsegment 917, thus indicating lane 929 as a pedestrian lane.

While not shown expressly herein, the vehicle icon 903 or pedestrianicon 927 can be presented in any other form, including textually oraudibly. Moreover, the location/position of a respective icon 903 or 927within a particular lane may be adapted to reflect the actual probepoint location determination. For example, in the case where the userwalks close to the edge of lane 1 and near lane 2, the pedestrian icon917 as presented would be skewed left (by a spatial distance d) todepict the position of the device user within the lane.

Still further, in future embodiments, it is contemplated that transportmode information and/or lane-level details regarding associatedtravelers may also be presented in connection with a requesting userdevice 900. For example, in the case where the user is in sharednavigation mode with another user that is driving while they arewalking, the representative transport mode icons would be different foreach user. In this case, the user's device would present a pedestrianicon 927 relative to their position/location while the other user's iconwould be presented to the user as a vehicle icon 903 relative to theother user's position/location.

Still further, while not shown expressly, the aggregated (persistentlyupdated) lane model information—reflective of the probe points/spatialdistribution patterns—may be subsequently retrieved. For example, thelane model information may be subsequently analyzed in connection withrelevant mapping information, traffic information, event data, by thecity planning department that hosted the festival (per the abovescenario) to ascertain traffic patterns, plan alternative or optimizedpedestrian routes, observe the most popular POIs, etc. Alternatively,said information can be retrieved by an autonomous vehicle navigationservice or event service for providing near-real time travelinstructions, event recommendations (e.g., most popular food vendorbased on foot traffic density), resources planning recommendations(e.g., least frequented portable restroom and thus most available basedon limited foot traffic density).

The processes described herein for determining probe data generated by adevice travelling on a road segment is for pedestrian travel may beadvantageously implemented via software, hardware, firmware or acombination of software and/or firmware and/or hardware. For example,the processes described herein, may be advantageously implemented viaprocessor(s), Digital Signal Processing (DSP) chip, an ApplicationSpecific Integrated Circuit (ASIC), Field Programmable Gate Arrays(FPGAs), etc. Such exemplary hardware for performing the describedfunctions is detailed below.

FIG. 10 illustrates a computer system 1000 upon which an embodiment ofthe invention may be implemented. Although computer system 1000 isdepicted with respect to a particular device or equipment, it iscontemplated that other devices or equipment (e.g., network elements,servers, etc.) within FIG. 10 can deploy the illustrated hardware andcomponents of system 1000. Computer system 1000 is programmed (e.g., viacomputer program code or instructions) to determine probe data generatedby a device travelling on a road segment is for pedestrian travel asdescribed herein and includes a communication mechanism such as a bus1010 for passing information between other internal and externalcomponents of the computer system 1000. Information (also called data)is represented as a physical expression of a measurable phenomenon,typically electric voltages, but including, in other embodiments, suchphenomena as magnetic, electromagnetic, pressure, chemical, biological,molecular, atomic, sub-atomic and quantum interactions. For example,north and south magnetic fields, or a zero and non-zero electricvoltage, represent two states (0, 1) of a binary digit (bit). Otherphenomena can represent digits of a higher base. A superposition ofmultiple simultaneous quantum states before measurement represents aquantum bit (qubit). A sequence of one or more digits constitutesdigital data that is used to represent a number or code for a character.In some embodiments, information called analog data is represented by anear continuum of measurable values within a particular range. Computersystem 1000, or a portion thereof, constitutes a means for performingone or more steps of determining probe data generated by a devicetravelling on a road segment is for pedestrian travel.

A bus 1010 includes one or more parallel conductors of information sothat information is transferred quickly among devices coupled to the bus1010. One or more processors 1002 for processing information are coupledwith the bus 1010.

A processor (or multiple processors) 1002 performs a set of operationson information as specified by computer program code related todetermine probe data generated by a device travelling on a road segmentis for pedestrian travel. The computer program code is a set ofinstructions or statements providing instructions for the operation ofthe processor and/or the computer system to perform specified functions.The code, for example, may be written in a computer programming languagethat is compiled into a native instruction set of the processor. Thecode may also be written directly using the native instruction set(e.g., machine language). The set of operations include bringinginformation in from the bus 1010 and placing information on the bus1010. The set of operations also typically include comparing two or moreunits of information, shifting positions of units of information, andcombining two or more units of information, such as by addition ormultiplication or logical operations like OR, exclusive OR (XOR), andAND. Each operation of the set of operations that can be performed bythe processor is represented to the processor by information calledinstructions, such as an operation code of one or more digits. Asequence of operations to be executed by the processor 1002, such as asequence of operation codes, constitute processor instructions, alsocalled computer system instructions or, simply, computer instructions.Processors may be implemented as mechanical, electrical, magnetic,optical, chemical or quantum components, among others, alone or incombination.

Computer system 1000 also includes a memory 1004 coupled to bus 1010.The memory 1004, such as a random access memory (RAM) or any otherdynamic storage device, stores information including processorinstructions for determining probe data generated by a device travellingon a road segment is for pedestrian travel. Dynamic memory allowsinformation stored therein to be changed by the computer system 1000.RAM allows a unit of information stored at a location called a memoryaddress to be stored and retrieved independently of information atneighboring addresses. The memory 1004 is also used by the processor1002 to store temporary values during execution of processorinstructions. The computer system 1000 also includes a read only memory(ROM) 1006 or any other static storage device coupled to the bus 1010for storing static information, including instructions, that is notchanged by the computer system 1000. Some memory is composed of volatilestorage that loses the information stored thereon when power is lost.Also coupled to bus 1010 is a non-volatile (persistent) storage device1008, such as a magnetic disk, optical disk or flash card, for storinginformation, including instructions, that persists even when thecomputer system 1000 is turned off or otherwise loses power.

Information, including instructions for determining probe data generatedby a device travelling on a road segment is for pedestrian travel, isprovided to the bus 1010 for use by the processor from an external inputdevice 1012, such as a keyboard containing alphanumeric keys operated bya human user, a microphone, an Infrared (IR) remote control, a joystick,a game pad, a stylus pen, a touch screen, or a sensor. A sensor detectsconditions in its vicinity and transforms those detections into physicalexpression compatible with the measurable phenomenon used to representinformation in computer system 1000. Other external devices coupled tobus 1010, used primarily for interacting with humans, include a displaydevice 1014, such as a cathode ray tube (CRT), a liquid crystal display(LCD), a light emitting diode (LED) display, an organic LED (OLED)display, a plasma screen, or a printer for presenting text or images,and a pointing device 1016, such as a mouse, a trackball, cursordirection keys, or a motion sensor, for controlling a position of asmall cursor image presented on the display 1014 and issuing commandsassociated with graphical elements presented on the display 1014. Insome embodiments, for example, in embodiments in which the computersystem 1000 performs all functions automatically without human input,one or more of external input device 1012, display device 1014 andpointing device 1016 is omitted.

In the illustrated embodiment, special purpose hardware, such as anapplication specific integrated circuit (ASIC) 1020, is coupled to bus1010. The special purpose hardware is configured to perform operationsnot performed by processor 1002 quickly enough for special purposes.Examples of ASICs include graphics accelerator cards for generatingimages for display 1014, cryptographic boards for encrypting anddecrypting messages sent over a network, speech recognition, andinterfaces to special external devices, such as robotic arms and medicalscanning equipment that repeatedly perform some complex sequence ofoperations that are more efficiently implemented in hardware.

Computer system 1000 also includes one or more instances of acommunications interface 1070 coupled to bus 1010. Communicationinterface 1070 provides a one-way or two-way communication coupling to avariety of external devices that operate with their own processors, suchas printers, scanners and external disks. In general, the coupling iswith a network link 1078 that is connected to a local network 1080 towhich a variety of external devices with their own processors areconnected. For example, communication interface 1070 may be a parallelport or a serial port or a universal serial bus (USB) port on a personalcomputer. In some embodiments, communications interface 1070 is anintegrated services digital network (ISDN) card or a digital subscriberline (DSL) card or a telephone modem that provides an informationcommunication connection to a corresponding type of telephone line. Insome embodiments, a communication interface 1070 is a cable modem thatconverts signals on bus 1010 into signals for a communication connectionover a coaxial cable or into optical signals for a communicationconnection over a fiber optic cable. As another example, communicationsinterface 1070 may be a local area network (LAN) card to provide a datacommunication connection to a compatible LAN, such as Ethernet. Wirelesslinks may also be implemented. For wireless links, the communicationsinterface 1070 sends or receives or both sends and receives electrical,acoustic or electromagnetic signals, including infrared and opticalsignals, that carry information streams, such as digital data. Forexample, in wireless handheld devices, such as mobile telephones likecell phones, the communications interface 1070 includes a radio bandelectromagnetic transmitter and receiver called a radio transceiver. Incertain embodiments, the communications interface 1070 enablesconnection to the communication network 105 for determining probe datagenerated by a device travelling on a road segment is for pedestriantravel to the UE 103.

The term “computer-readable medium” as used herein refers to any mediumthat participates in providing information to processor 1002, includinginstructions for execution. Such a medium may take many forms,including, but not limited to computer-readable storage medium (e.g.,non-volatile media, volatile media), and transmission media.Non-transitory media, such as non-volatile media, include, for example,optical or magnetic disks, such as storage device 1008. Volatile mediainclude, for example, dynamic memory 1004. Transmission media include,for example, twisted pair cables, coaxial cables, copper wire, fiberoptic cables, and carrier waves that travel through space without wiresor cables, such as acoustic waves and electromagnetic waves, includingradio, optical and infrared waves. Signals include man-made transientvariations in amplitude, frequency, phase, polarization or otherphysical properties transmitted through the transmission media. Commonforms of computer-readable media include, for example, a floppy disk, aflexible disk, hard disk, magnetic tape, any other magnetic medium, aCD-ROM, CDRW, DVD, any other optical medium, punch cards, paper tape,optical mark sheets, any other physical medium with patterns of holes orother optically recognizable indicia, a RAM, a PROM, an EPROM, aFLASH-EPROM, an EEPROM, a flash memory, any other memory chip orcartridge, a carrier wave, or any other medium from which a computer canread. The term computer-readable storage medium is used herein to referto any computer-readable medium except transmission media.

Logic encoded in one or more tangible media includes one or both ofprocessor instructions on a computer-readable storage media and specialpurpose hardware, such as ASIC 1020.

Network link 1078 typically provides information communication usingtransmission media through one or more networks to other devices thatuse or process the information. For example, network link 1078 mayprovide a connection through local network 1080 to a host computer 1082or to equipment 1084 operated by an Internet Service Provider (ISP). ISPequipment 1084 in turn provides data communication services through thepublic, world-wide packet-switching communication network of networksnow commonly referred to as the Internet 1090.

A computer called a server host 1092 connected to the Internet hosts aprocess that provides a service in response to information received overthe Internet. For example, server host 1092 hosts a process thatprovides information representing video data for presentation at display1014. It is contemplated that the components of system 1000 can bedeployed in various configurations within other computer systems, e.g.,host 1082 and server 1092.

At least some embodiments of the invention are related to the use ofcomputer system 1000 for implementing some or all of the techniquesdescribed herein. According to one embodiment of the invention, thosetechniques are performed by computer system 1000 in response toprocessor 1002 executing one or more sequences of one or more processorinstructions contained in memory 1004. Such instructions, also calledcomputer instructions, software and program code, may be read intomemory 1004 from another computer-readable medium such as storage device1008 or network link 1078. Execution of the sequences of instructionscontained in memory 1004 causes processor 1002 to perform one or more ofthe method steps described herein. In alternative embodiments, hardware,such as ASIC 1020, may be used in place of or in combination withsoftware to implement the invention. Thus, embodiments of the inventionare not limited to any specific combination of hardware and software,unless otherwise explicitly stated herein.

The signals transmitted over network link 1078 and other networksthrough communications interface 1070, carry information to and fromcomputer system 1000. Computer system 1000 can send and receiveinformation, including program code, through the networks 1080, 1090among others, through network link 1078 and communications interface1070. In an example using the Internet 1090, a server host 1092transmits program code for a particular application, requested by amessage sent from computer 1000, through Internet 1090, ISP equipment1084, local network 1080 and communications interface 1070. The receivedcode may be executed by processor 1002 as it is received, or may bestored in memory 1004 or in storage device 1008 or any othernon-volatile storage for later execution, or both. In this manner,computer system 1000 may obtain application program code in the form ofsignals on a carrier wave.

Various forms of computer readable media may be involved in carrying oneor more sequence of instructions or data or both to processor 1002 forexecution. For example, instructions and data may initially be carriedon a magnetic disk of a remote computer such as host 1082. The remotecomputer loads the instructions and data into its dynamic memory andsends the instructions and data over a telephone line using a modem. Amodem local to the computer system 1000 receives the instructions anddata on a telephone line and uses an infra-red transmitter to convertthe instructions and data to a signal on an infra-red carrier waveserving as the network link 1078. An infrared detector serving ascommunications interface 1070 receives the instructions and data carriedin the infrared signal and places information representing theinstructions and data onto bus 1010. Bus 1010 carries the information tomemory 1004 from which processor 1002 retrieves and executes theinstructions using some of the data sent with the instructions. Theinstructions and data received in memory 1004 may optionally be storedon storage device 1008, either before or after execution by theprocessor 1002.

FIG. 11 illustrates a chip set or chip 1100 upon which an embodiment ofthe invention may be implemented. Chip set 1100 is programmed todetermine probe data generated by a device travelling on a road segmentis for pedestrian travel as described herein and includes, for instance,the processor and memory components described with respect to FIG. 10incorporated in one or more physical packages (e.g., chips). By way ofexample, a physical package includes an arrangement of one or morematerials, components, and/or wires on a structural assembly (e.g., abaseboard) to provide one or more characteristics such as physicalstrength, conservation of size, and/or limitation of electricalinteraction. It is contemplated that in certain embodiments the chip set1100 can be implemented in a single chip. It is further contemplatedthat in certain embodiments the chip set or chip 1100 can be implementedas a single “system on a chip.” It is further contemplated that incertain embodiments a separate ASIC would not be used, for example, andthat all relevant functions as disclosed herein would be performed by aprocessor or processors. Chip set or chip 1100, or a portion thereof,constitutes a means for performing one or more steps of providing userinterface navigation information associated with the availability offunctions. Chip set or chip 1100, or a portion thereof, constitutes ameans for performing one or more steps of determining probe datagenerated by a device travelling on a road segment is for pedestriantravel.

In one embodiment, the chip set or chip 1100 includes a communicationmechanism such as a bus 1101 for passing information among thecomponents of the chip set 1100. A processor 1103 has connectivity tothe bus 1101 to execute instructions and process information stored in,for example, a memory 1105. The processor 1103 may include one or moreprocessing cores with each core configured to perform independently. Amulti-core processor enables multiprocessing within a single physicalpackage. Examples of a multi-core processor include two, four, eight, orgreater numbers of processing cores. Alternatively, or in addition, theprocessor 1103 may include one or more microprocessors configured intandem via the bus 1101 to enable independent execution of instructions,pipelining, and multithreading. The processor 1103 may also beaccompanied with one or more specialized components to perform certainprocessing functions and tasks such as one or more digital signalprocessors (DSP) 1107, or one or more application-specific integratedcircuits (ASIC) 1109. A DSP 1107 typically is configured to processreal-world signals (e.g., sound) in real time independently of theprocessor 1103. Similarly, an ASIC 1109 can be configured to performedspecialized functions not easily performed by a more general purposeprocessor. Other specialized components to aid in performing theinventive functions described herein may include one or more fieldprogrammable gate arrays (FPGA), one or more controllers, or one or moreother special-purpose computer chips.

In one embodiment, the chip set or chip 1100 includes merely one or moreprocessors and some software and/or firmware supporting and/or relatingto and/or for the one or more processors.

The processor 1103 and accompanying components have connectivity to thememory 1105 via the bus 1101. The memory 1105 includes both dynamicmemory (e.g., RAM, magnetic disk, writable optical disk, etc.) andstatic memory (e.g., ROM, CD-ROM, etc.) for storing executableinstructions that when executed perform the inventive steps describedherein to determine probe data generated by a device travelling on aroad segment is for pedestrian travel. The memory 1105 also stores thedata associated with or generated by the execution of the inventivesteps.

FIG. 12 is a diagram of exemplary components of a mobile terminal (e.g.,handset) for communications, which is capable of operating in the systemof FIG. 1, according to one embodiment. In some embodiments, mobileterminal 1201, or a portion thereof, constitutes a means for performingone or more steps of determining probe data generated by a devicetravelling on a road segment is for pedestrian travel. Generally, aradio receiver is often defined in terms of front-end and back-endcharacteristics. The front-end of the receiver encompasses all of theRadio Frequency (RF) circuitry whereas the back-end encompasses all ofthe base-band processing circuitry. As used in this application, theterm “circuitry” refers to both: (1) hardware-only implementations (suchas implementations in only analog and/or digital circuitry), and (2) tocombinations of circuitry and software (and/or firmware) (such as, ifapplicable to the particular context, to a combination of processor(s),including digital signal processor(s), software, and memory(ies) thatwork together to cause an apparatus, such as a mobile phone or server,to perform various functions). This definition of “circuitry” applies toall uses of this term in this application, including in any claims. As afurther example, as used in this application and if applicable to theparticular context, the term “circuitry” would also cover animplementation of merely a processor (or multiple processors) and its(or their) accompanying software/or firmware. The term “circuitry” wouldalso cover if applicable to the particular context, for example, abaseband integrated circuit or applications processor integrated circuitin a mobile phone or a similar integrated circuit in a cellular networkdevice or other network devices.

Pertinent internal components of the telephone include a Main ControlUnit (MCU) 1203, a Digital Signal Processor (DSP) 1205, and areceiver/transmitter unit including a microphone gain control unit and aspeaker gain control unit. A main display unit 1207 provides a displayto the user in support of various applications and mobile terminalfunctions that perform or support the steps of determining probe datagenerated by a device travelling on a road segment is for pedestriantravel. The display 1207 includes display circuitry configured todisplay at least a portion of a user interface of the mobile terminal(e.g., mobile telephone). Additionally, the display 1207 and displaycircuitry are configured to facilitate user control of at least somefunctions of the mobile terminal. An audio function circuitry 1209includes a microphone 1211 and microphone amplifier that amplifies thespeech signal output from the microphone 1211. The amplified speechsignal output from the microphone 1211 is fed to a coder/decoder (CODEC)1213.

A radio section 1215 amplifies power and converts frequency in order tocommunicate with a base station, which is included in a mobilecommunication system, via antenna 1217. The power amplifier (PA) 1219and the transmitter/modulation circuitry are operationally responsive tothe MCU 1203, with an output from the PA 1219 coupled to the duplexer1221 or circulator or antenna switch, as known in the art. The PA 1219also couples to a battery interface and power control unit 1220.

In use, a user of mobile terminal 1201 speaks into the microphone 1211and his or her voice along with any detected background noise isconverted into an analog voltage. The analog voltage is then convertedinto a digital signal through the Analog to Digital Converter (ADC)1223. The control unit 1203 routes the digital signal into the DSP 1205for processing therein, such as speech encoding, channel encoding,encrypting, and interleaving. In one embodiment, the processed voicesignals are encoded, by units not separately shown, using a cellulartransmission protocol such as enhanced data rates for global evolution(EDGE), general packet radio service (GPRS), global system for mobilecommunications (GSM), Internet protocol multimedia subsystem (IMS),universal mobile telecommunications system (UMTS), etc., as well as anyother suitable wireless medium, e.g., microwave access (WiMAX), LongTerm Evolution (LTE) networks, code division multiple access (CDMA),wideband code division multiple access (WCDMA), wireless fidelity(WiFi), satellite, and the like, or any combination thereof.

The encoded signals are then routed to an equalizer 1225 forcompensation of any frequency-dependent impairments that occur duringtransmission though the air such as phase and amplitude distortion.After equalizing the bit stream, the modulator 1227 combines the signalwith a RF signal generated in the RF interface 1229. The modulator 1227generates a sine wave by way of frequency or phase modulation. In orderto prepare the signal for transmission, an up-converter 1231 combinesthe sine wave output from the modulator 1227 with another sine wavegenerated by a synthesizer 1233 to achieve the desired frequency oftransmission. The signal is then sent through a PA 1219 to increase thesignal to an appropriate power level. In practical systems, the PA 1219acts as a variable gain amplifier whose gain is controlled by the DSP1205 from information received from a network base station. The signalis then filtered within the duplexer 1221 and optionally sent to anantenna coupler 1235 to match impedances to provide maximum powertransfer. Finally, the signal is transmitted via antenna 1217 to a localbase station. An automatic gain control (AGC) can be supplied to controlthe gain of the final stages of the receiver. The signals may beforwarded from there to a remote telephone which may be another cellulartelephone, any other mobile phone or a land-line connected to a PublicSwitched Telephone Network (PSTN), or other telephony networks.

Voice signals transmitted to the mobile terminal 1201 are received viaantenna 1217 and immediately amplified by a low noise amplifier (LNA)1237. A down-converter 1239 lowers the carrier frequency while thedemodulator 1241 strips away the RF leaving only a digital bit stream.The signal then goes through the equalizer 1225 and is processed by theDSP 1205. A Digital to Analog Converter (DAC) 1243 converts the signaland the resulting output is transmitted to the user through the speaker1245, all under control of a Main Control Unit (MCU) 1203 which can beimplemented as a Central Processing Unit (CPU).

The MCU 1203 receives various signals including input signals from thekeyboard 1247. The keyboard 1247 and/or the MCU 1203 in combination withother user input components (e.g., the microphone 1211) comprise a userinterface circuitry for managing user input. The MCU 1203 runs a userinterface software to facilitate user control of at least some functionsof the mobile terminal 1201 to determine probe data generated by adevice travelling on a road segment is for pedestrian travel. The MCU1203 also delivers a display command and a switch command to the display1207 and to the speech output switching controller, respectively.Further, the MCU 1203 exchanges information with the DSP 1205 and canaccess an optionally incorporated SIM card 1249 and a memory 1251. Inaddition, the MCU 1203 executes various control functions required ofthe terminal. The DSP 1205 may, depending upon the implementation,perform any of a variety of conventional digital processing functions onthe voice signals. Additionally, DSP 1205 determines the backgroundnoise level of the local environment from the signals detected bymicrophone 1211 and sets the gain of microphone 1211 to a level selectedto compensate for the natural tendency of the user of the mobileterminal 1201.

The CODEC 1213 includes the ADC 1223 and DAC 1243. The memory 1251stores various data including call incoming tone data and is capable ofstoring other data including music data received via, e.g., the globalInternet. The software module could reside in RAM memory, flash memory,registers, or any other form of writable storage medium known in theart. The memory device 1251 may be, but not limited to, a single memory,CD, DVD, ROM, RAM, EEPROM, optical storage, magnetic disk storage, flashmemory storage, or any other non-volatile storage medium capable ofstoring digital data.

An optionally incorporated SIM card 1249 carries, for instance,important information, such as the cellular phone number, the carriersupplying service, subscription details, and security information. TheSIM card 1249 serves primarily to identify the mobile terminal 1201 on aradio network. The card 1249 also contains a memory for storing apersonal telephone number registry, text messages, and user specificmobile terminal settings.

While the invention has been described in connection with a number ofembodiments and implementations, the invention is not so limited butcovers various obvious modifications and equivalent arrangements, whichfall within the purview of the appended claims. Although features of theinvention are expressed in certain combinations among the claims, it iscontemplated that these features can be arranged in any combination andorder.

What is claimed is:
 1. A computer-implemented method for identifying atransport mode of a probe point comprising: determining, by a processor,a speed of a probe point; determining, by the processor, a spatialdistance of the probe point from a center line vector of a road segment;determining, by the processor, an allowed transport mode for the roadsegment; and identifying, by the processor, the transport mode of theprobe point based on the speed, the location of the probe point withrespect to the center line, and the allowed transport mode, wherein thetransport mode, the allowed transport mode, or a combination thereofincludes a car transport mode or a pedestrian transport mode.
 2. Themethod of claim 1, further comprising: clustering a plurality ofhistorical probe points into one or more spatial distribution clustersalong a length of the road segment; and determining one or morecross-sections of the one or more spatial distribution clusters withrespect to the length of the road segment, wherein the one or morespatial distribution clusters corresponds to one or more travel lanes ofthe road segment and the center line vector is based on a center line ofthe at least one of the spatial distribution clusters.
 3. The method ofclaim 1, further comprising: calculating a probability the probe pointis either the car transport mode or the pedestrian transport mode basedon the spatial distance, wherein the transport mode of the probe pointis further based on the probability.
 4. The method of claim 3, whereinthe probability is a probability the probe point is a pedestriantransport mode, the method further comprising: increasing theprobability as the spatial distance from the center line vectorincreases.
 5. The method of claim 1, further comprising: determining afirst probability the probe point is using the car transport mode basedon the speed of the probe point; and determining a second probabilitythe probe point is using the pedestrian mode based on the speed of theprobe point, wherein the transport mode of the probe point is furtherbased on the first probability, the second probability, or a combinationthereof.
 6. The method of claim 1, further comprising: increasing afirst probability the mode of transport is the car mode of transportbased on a determination the allowed transport mode is a car transportmode; and increasing a second probability the mode of transport is thepedestrian mode of transport based on a determination the allowedtransport mode is a pedestrian transport mode, wherein the transportmode of the probe point is further based on the first probability, thesecond probability, or a combination thereof.
 7. The method of claim 1,wherein the probe point is part of a sequence of probe points generatedby a probe device and wherein the sequence of probe points is a mixedmode sequence comprising a plurality of transport modes.
 8. The methodof claim 7, further comprising: determining a transition point between afirst transport mode and a second transport mode during the sequence,wherein the probe point occurs at the transition point.
 9. The method ofclaim 8, wherein respective transport modes of a plurality of otherprobe points occurring in the sequence of probe points before or afterthe transition point is identified based on the transport modeidentified for the probe point at the transition point.
 10. An apparatusfor determining pedestrian travel comprising: at least one processor;and at least one memory including computer program code for one or moreprograms, the at least one memory and the computer program codeconfigured to, with the at least one processor, cause the apparatus toperform at least the following, determine a speed of a probe point;determine a spatial distance of the probe point from a center linevector of a road segment; determine an allowed transport mode for theroad segment; and identify the transport mode of the probe point basedon the speed, the location of the probe point with respect to the centerline, and the allowed transport mode, wherein the transport mode, theallowed transport mode, or a combination thereof includes a cartransport mode or a pedestrian transport mode.
 11. The apparatus ofclaim 10, wherein the apparatus is further caused to: cluster aplurality of historical probe points into one or more spatialdistribution clusters along a length of the road segment; and determineone or more cross-sections of the one or more spatial distributionclusters with respect to the length of the road segment, wherein the oneor more spatial distribution clusters corresponds to one or more travellanes of the road segment and the center line vector is based on acenter line of the at least one of the spatial distribution clusters.12. The apparatus of claim 11, wherein the step of matching is furthercaused to: calculate a probability the probe point is either the cartransport mode or the pedestrian transport mode based on the spatialdistance, wherein the transport mode of the probe point is further basedon the probability.
 13. The apparatus of claim 12, wherein theprobability is a probability the probe point is a pedestrian transportmode, the apparatus further caused to: increase the probability as thespatial distance from the center line vector increases.
 14. Theapparatus of claim 10, wherein the apparatus is further caused to:determine a first probability the mode of transport is the car mode oftransport based on a determination the allowed transport mode is a cartransport mode; and determine a second probability the mode of transportis the pedestrian mode of transport based on a determination the allowedtransport mode is a pedestrian transport mode, wherein the transportmode of the probe point is further based on the first probability, thesecond probability, or a combination thereof.
 15. The apparatus of claim10, wherein the apparatus is further caused to: increase a firstprobability the mode of transport is the car mode of transport based ona determination the allowed transport mode is a car transport mode; andincrease a second probability the mode of transport is the pedestrianmode of transport based on a determination the allowed transport mode isa pedestrian transport mode, wherein the transport mode of the probepoint is further based on the first probability, the second probability,or a combination thereof.
 16. The apparatus of claim 15, wherein theapparatus is further caused to: determine a transition point between afirst transport mode and a second transport mode during a sequence ofprobe points generated by a probe device, wherein the probe point occursat the transition point and is part of the sequence of probe points andwherein the sequence of probe points is a mixed mode sequence comprisinga plurality of transport modes.
 17. A non-transitory computer-readablestorage medium carrying one or more sequences of one or moreinstructions which, when executed by one or more processors, cause anapparatus to at least perform the following steps: determining, by aprocessor, a speed of a probe point; determining, by the processor, aspatial distance of the probe point from a center line vector of a roadsegment; determining, by the processor, an allowed transport mode forthe road segment; and identifying, by the processor, the transport modeof the probe point based on the speed, the location of the probe pointwith respect to the center line, and the allowed transport mode, whereinthe transport mode, the allowed transport mode, or a combination thereofincludes a car transport mode or a pedestrian transport mode.
 18. Thenon-transitory computer-readable storage medium of claim 16, wherein theapparatus is further caused to perform: clustering a plurality ofhistorical probe points into one or more spatial distribution clustersalong a length of the road segment; and determining one or morecross-sections of the one or more spatial distribution clusters withrespect to the length of the road segment, wherein the one or morespatial distribution clusters corresponds to one or more travel lanes ofthe road segment and the center line vector is based on a center line ofthe at least one of the spatial distribution clusters.
 19. Thenon-transitory computer-readable storage medium of claim 17, wherein thestep of matching is further caused to perform: determining a transitionpoint between a first transport mode and a second transport mode duringa sequence of probe points generated by a probe device, wherein theprobe point occurs at the transition point and is part of the sequenceof probe points and wherein the sequence of probe points is a mixed modesequence comprising a plurality of transport modes.
 20. Thenon-transitory computer-readable storage medium of claim 18, whereinrespective transport modes of a plurality of other probe pointsoccurring in the sequence of probe points before or after the transitionpoint is identified based on the transport mode identified for the probepoint at the transition point.